Tunable Electrophoretic Separations Using a Scalable, Fabric-Based Platform
Bibliographic record
Abstract
There is a rising need for low-cost and scalable platforms for sensitive medical diagnostic testing. Fabric weaving is a mature, scalable manufacturing technology and can be used as a platform to manufacture microfluidic diagnostic tests with controlled, tunable flow. Given its scalability, low manufacturing cost (<$0.25 per device), and potential for patterning multiplexed channel geometries, fabric is a viable platform for the development of analytical devices. In this paper, we describe a fabric-based electrophoretic platform for protein separation. Appropriate yarns were selected for each region of the device and weaved into straight channel electrophoretic chips in a single step. A wide dynamic range of analyte molecules ranging from small molecule dyes (<1 kDa) to macromolecule proteins (67-150 kDa) were separated in the device. Individual yarns behave as a chromatographic medium for electrophoresis. We therefore explored the effect of yarn and fabric parameters on separation resolution. Separation speed and resolution were enhanced by increasing the number of yarns per unit area of fabric and decreasing yarn hydrophilicity. However, for protein analytes that often require hydrophilic, passivated surfaces, these effects need to be properly tuned to achieve well-resolved separations. A fabric device tuned for protein separations was built and demonstrated. As an analytical output parameter for this device, the electrophoretic mobility of a sedimentation marker, Naphthol Blue Black bovine albumin in glycine-NaOH buffer, pH 8.58 was estimated and found to be -2.7 × 10(-8) m(2) V(-1) s(-1). The ability to tune separation may be used to predefine regions in the fabric for successive preconcentrations and separations. The device may then be applied for the multiplexed detection of low abundance proteins from complex biological samples such as serum and cell lysate.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".